def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
raise NotImplementedError( ` args `)
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error(
"incorrect number of arguments (expected upto 2 but got %s)" %
(len(args)))
options.kernel_path = fix_path(options.kernel_path)
options.input_path = fix_path(options.input_path)
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = '_convolved' % ()
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
kernel = ImageStack.load(options.kernel_path, options=options)
stack = ImageStack.load(options.input_path, options=options)
result = convolve(kernel.images, stack.images, options=options)
if 1:
print 'Saving result to', options.output_path
ImageStack(result, stack.pathinfo).save(options.output_path)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
raise NotImplementedError (`args`)
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error("incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
options.kernel_path = fix_path(options.kernel_path)
options.input_path = fix_path(options.input_path)
if options.output_path is None:
b,e = os.path.splitext(options.input_path)
suffix = '_convolved' % ()
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
kernel = ImageStack.load(options.kernel_path, options=options)
stack = ImageStack.load(options.input_path, options=options)
result = convolve (kernel.images, stack.images, options=options)
if 1:
print 'Saving result to',options.output_path
ImageStack(result, stack.pathinfo).save(options.output_path)
from numpy import *
from scipy.stats import poisson
from iocbio.ops import convolve
kernel = array([0, 1, 2, 2, 2, 1, 0])
x = arange(0, 2 * pi, 0.1)
data = 50 + 7 * sin(x) + 5 * sin(2 * x)
data_with_noise = poisson.rvs(data)
data_convolved = convolve(kernel, data_with_noise)
from matplotlib import pyplot as plt
plt.plot(x, data, label='data')
plt.plot(x, data_with_noise, 'o', label='data with poisson noise')
plt.plot(x, data_convolved, label='convolved noisy data')
plt.legend()
plt.xlabel('x')
plt.ylabel('data')
plt.title('Convolution with %s for recovering data=50+7*sin(x)+5*sin(2*x)' %
(kernel))
plt.savefig('convolve_1d.png')
plt.show()
from numpy import *
from scipy.stats import poisson
from iocbio.ops import convolve
kernel = array([0,1,2,2,2,1,0])
x = arange(0,2*pi,0.1)
data = 50+7*sin(x)+5*sin(2*x)
data_with_noise = poisson.rvs(data)
data_convolved = convolve(kernel, data_with_noise)
from matplotlib import pyplot as plt
plt.plot (x,data,label='data')
plt.plot (x,data_with_noise,'o',label='data with poisson noise')
plt.plot (x,data_convolved,label='convolved noisy data')
plt.legend ()
plt.xlabel ('x')
plt.ylabel ('data')
plt.title ('Convolution with %s for recovering data=50+7*sin(x)+5*sin(2*x)' % (kernel))
plt.savefig('convolve_1d.png')
plt.show ()
import numpy
from iocbio.ops import convolve
from iocbio.microscope.deconvolution import deconvolve
from iocbio.io import ImageStack
import scipy.stats
from matplotlib import pyplot as plt
kernel = numpy.array([0, 1, 3, 1, 0])
test_data = numpy.array([0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0]) * 50
data = convolve(kernel, test_data)
degraded_data = scipy.stats.poisson.rvs(numpy.where(data <= 0, 1e-16,
data)).astype(data.dtype)
psf = ImageStack(kernel, voxel_sizes=(1, ))
stack = ImageStack(degraded_data, voxel_sizes=(1, ))
deconvolved_data = deconvolve(psf, stack).images
plt.plot(test_data, label='test')
plt.plot(data, label='convolved')
plt.plot(degraded_data, label='degraded')
plt.plot(deconvolved_data, label='deconvolved')
plt.legend()
plt.ylabel('data')
plt.xlabel('index')
plt.title('Deconvolving degraded test data.')
plt.savefig('deconvolve_poisson_1d.png')
plt.show()
import numpy
from iocbio.ops import convolve
from iocbio.microscope.deconvolution import deconvolve
from iocbio.io import ImageStack
import scipy.stats
from matplotlib import pyplot as plt
kernel = numpy.array([0,1,3,1,0])
test_data = numpy.array([0, 0,0,0,2, 0,0,0,0, 0,1,0,1, 0,0,0])*50
data =convolve(kernel, test_data)
degraded_data = scipy.stats.poisson.rvs(numpy.where(data<=0, 1e-16, data)).astype(data.dtype)
psf = ImageStack(kernel, voxel_sizes = (1,))
stack = ImageStack(degraded_data, voxel_sizes = (1,))
deconvolved_data = deconvolve(psf, stack).images
plt.plot(test_data, label='test')
plt.plot (data, label='convolved')
plt.plot (degraded_data, label='degraded')
plt.plot (deconvolved_data, label='deconvolved')
plt.legend()
plt.ylabel('data')
plt.xlabel('index')
plt.title('Deconvolving degraded test data.')
plt.savefig('deconvolve_poisson_1d.png')
plt.show ()
